Managing passthru connections on an operator graph

ABSTRACT

Embodiments of the disclosure provide a method, system, and computer program product for processing data such as a stream of tuples. Each tuple can contain one or more attributes. The method can include processing the attributes of the stream of tuples using stream operators operating on one or more computer processors and corresponding to one or more processing elements. The method can also include detecting an indicative element from a plurality of stream operators. The method can also include transmitting, in response to detecting the indicative element, a passthru command to a processing element corresponding to the indicative element. The method can also include altering, in response to receiving the passthru command at the processing element, a portion of attribute processing for the indicative element.

FIELD

This disclosure generally relates to stream computing, and inparticular, to computing applications that receive streaming data andprocess the data as it is received.

BACKGROUND

Database systems are typically configured to separate the process ofstoring data from accessing, manipulating, or using data stored in adatabase. More specifically, database systems use a model in which datais first stored and indexed in a memory before subsequent querying andanalysis. In general, database systems may not be well suited forperforming real-time processing and analyzing streaming data. Inparticular, database systems may be unable to store, index, and analyzelarge amounts of streaming data efficiently or in real time.

SUMMARY

Embodiments of the disclosure provide a method, system, and computerprogram product for processing data. The method, system, and computerprogram product receive two or more tuples to be processed by aplurality of processing elements operating on one or more computerprocessors.

One embodiment is directed toward a method for processing a stream oftuples. Each tuple can contain one or more attributes. The method caninclude processing the attributes of the stream of tuples using streamoperators operating on one or more computer processors and correspondingto one or more processing elements. The method can also includedetecting an indicative element from a plurality of stream operators.The method can also include transmitting, in response to detecting theindicative element, a passthru command to a processing elementcorresponding to the indicative element. The method can also includealtering, in response to receiving the passthru command at theprocessing element, a portion of attribute processing for the indicativeelement.

Another embodiment is directed toward a computer program product forprocessing a stream of tuples. Each tuple can contain one or moreattributes. The computer program product can comprise a computerreadable storage medium having program code embodied therewith. Theprogram code can comprise computer readable program code configured toprocess the attributes of the stream of tuples using stream operatorsoperating on one or more computer processors and corresponding one ormore processing elements. The program code can comprise computerreadable program code configured to detect an indicative element from aplurality of stream operators. The program code can comprise computerreadable program code configured to transmit, in response to detectingthe indicative element, a passthru command to a processing elementcorresponding to the indicative element. The program code can comprisecomputer readable program code configured to alter, in response toreceiving the passthru command at the processing element, a portion ofattribute processing for the indicative element.

Another embodiment is directed toward a system for processing a streamof tuples. Each tuple can have one or more attributes. The system caninclude one or more processing elements operating on one or morecomputer processors configured to processes the attributes from thestream of tuples. The system can include a stream manager configured toalter a portion of the attribute processing for an indicative element inresponse to the processing element receiving a passthru command. Thestream manager can further include a stream operator monitor configuredto detect the indicative element from a plurality of stream operators inthe one or more processing elements. The stream manager can furtherinclude a passthru manager configured to transmit the passthru commandin response to detecting the indicative element.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a computing infrastructure configured to execute astream computing application according to various embodiments.

FIG. 2 illustrates a more detailed view of a compute node of FIG. 1according to various embodiments.

FIG. 3 illustrates a more detailed view of the management system of FIG.1 according to various embodiments.

FIG. 4 illustrates a more detailed view of the compiler system of FIG. 1according to various embodiments.

FIG. 5 illustrates an operator graph for a stream computing applicationaccording to various embodiments.

FIG. 6 illustrates a flowchart of a method of processing a stream oftuples using the passthru feature, according to various embodiments.

FIG. 7 illustrates a flowchart of a method of selecting a passthrumethod, according to various embodiments.

FIG. 8 illustrates an exemplary operator graph that uses one or morepassthru features, according to various embodiments.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

Aspects of the present disclosure are generally directed toward a methodof processing attributes. The attributes may be a part of a tuple from astream of tuples. The stream of tuples may be processed using streamoperators on an operator graph. An operator graph may be on one or moreprocessing elements. The method may detect an indicative element fromthe stream operators. The method may include altering at least a portionof the processing of the indicative element to process the stream oftuples. Although not necessarily limited thereto, embodiments of thepresent disclosure can be appreciated in the context of streaming dataand problems relating to indicative elements that process the stream ofdata. Throughout this disclosure, the term stream operator may beabbreviated “S.O.”

Stream-based computing and stream-based database computing are emergingas a developing technology for database systems. Products are availablewhich allow users to create applications that process and querystreaming data before it reaches a database file. With this emergingtechnology, users can specify processing logic to apply to inbound datarecords while they are “in flight,” with the results available in a veryshort amount of time, often in fractions of a second. Constructing anapplication using this type of processing has opened up a newprogramming paradigm that will allow for development of a broad varietyof innovative applications, systems, and processes, as well as presentnew challenges for application programmers and database developers.

In a stream computing application, stream operators are connected to oneanother such that data flows from one stream operator to the next (e.g.,over a TCP/IP socket). When a stream operator receives data, it mayperform operations, such as analysis logic, which may change the tupleby adding or subtracting attributes, or updating the values of existingattributes within the tuple. When the analysis logic is complete, a newtuple is then sent to the next stream operator. Scalability is achievedby distributing an application across nodes by creating executables(i.e., processing elements), as well as replicating processing elementson multiple nodes and load balancing among them. Stream operators in astream computing application can be fused together to form a processingelement that is executable. Doing so allows processing elements to sharea common process space, resulting in much faster communication betweenstream operators than is available using inter-process communicationtechniques (e.g., using a TCP/IP socket). Further, processing elementscan be inserted or removed dynamically from an operator graphrepresenting the flow of data through the stream computing application.A particular stream operator may not reside within the same operatingsystem process as other stream operators. In addition, stream operatorsin the same operator graph may be hosted on different nodes, e.g., ondifferent compute nodes or on different cores of a compute node.

Data flows from one stream operator to another in the form of a “tuple.”A tuple is a sequence of one or more attributes associated with anentity. Attributes may be any of a variety of different types, e.g.,integer, float, Boolean, string, etc. The attributes may be ordered. Inaddition to attributes associated with an entity, a tuple may includemetadata, i.e., data about the tuple. A tuple may be extended by addingone or more additional attributes or metadata to it. As used herein,“stream” or “data stream” refers to a sequence of tuples. Generally, astream may be considered a pseudo-infinite sequence of tuples.

Nonetheless, an output tuple may be changed in some way by a streamoperator or processing element. An attribute or metadata may be added,deleted, or modified. For example, a tuple will often have two or moreattributes. A stream operator or processing element may receive thetuple having multiple attributes and output a tuple corresponding withthe input tuple. The stream operator or processing element may onlychange one of the attributes so that all of the attributes of the outputtuple except one are the same as the attributes of the input tuple.

Generally, a particular tuple output by a stream operator or processingelement may not be considered to be the same tuple as a correspondinginput tuple even if the input tuple is not changed by the processingelement. However, to simplify the present description and the claims, anoutput tuple that has the same data attributes or is associated with thesame entity as a corresponding input tuple will be referred to herein asthe same tuple unless the context or an express statement indicatesotherwise.

Stream computing applications handle massive volumes of data that needto be processed efficiently and in real time. For example, a streamcomputing application may continuously ingest and analyze hundreds ofthousands of messages per second and up to petabytes of data per day.Accordingly, each stream operator in a stream computing application maybe required to process a received tuple within fractions of a second.Unless the stream operators are located in the same processing element,it is necessary to use an inter-process communication path each time atuple is sent from one stream operator to another. Inter-processcommunication paths can be a critical resource in a stream computingapplication. According to various embodiments, the available bandwidthon one or more inter-process communication paths may be conserved.Efficient use of inter-process communication bandwidth can speed upprocessing.

FIG. 1 illustrates one exemplary computing infrastructure 100 that maybe configured to execute a stream computing application, according tosome embodiments. The computing infrastructure 100 includes a managementsystem 105 and two or more compute nodes 110A-110D—i.e., hosts—which arecommunicatively coupled to each other using one or more communicationsnetworks 120. The communications network 120 may include one or moreservers, networks, or databases, and may use a particular communicationprotocol to transfer data between the compute nodes 110A-110D. Acompiler system 102 may be communicatively coupled with the managementsystem 105 and the compute nodes 110 either directly or via thecommunications network 120.

The management system 105 can control the management of the computenodes 110A-110D (discussed further on FIG. 3). The management system 105can have an operator graph 132 with one or more stream operators and astream manager 134 to control the management of the stream of tuples inthe operator graph 132. The stream manager 134 can have components suchas a stream operator monitor 140 and a passthru manager 145. The streamoperator monitor 140 can detect an indicative element (discussed below)and communicate the indicative element to the stream manager 134,according to various embodiments. The passthru manager 145 can managethe implementation of a passthru feature for the indicative element.

The stream operators in the operator graph 132 can have an indicativeelement. An indicative element can be a stream operator that does nottransmit a processed attribute (explained below). The lack of receipt ofa tuple with a processed attribute can indicate that the element, e.g.,a stream operator or a processing element, has a fault. In order toidentify an indicative element and enable a passthru feature, componentsof a stream manager 134 can be used. For example, a stream operatormonitor 140 can identify an indicative element, a selection interface340 (in FIG. 3) can allow a user to enable the passthru feature, and thepassthru manager 145 can control when an indicative element has thepassthru feature activated.

The communications network 120 may include a variety of types ofphysical communication channels or “links.” The links may be wired,wireless, optical, or any other suitable media. In addition, thecommunications network 120 may include a variety of network hardware andsoftware for performing routing, switching, and other functions, such asrouters, switches, or bridges. The communications network 120 may bededicated for use by a stream computing application or shared with otherapplications and users. The communications network 120 may be any size.For example, the communications network 120 may include a single localarea network or a wide area network spanning a large geographical area,such as the Internet. The links may provide different levels ofbandwidth or capacity to transfer data at a particular rate. Thebandwidth that a particular link provides may vary depending on avariety of factors, including the type of communication media andwhether particular network hardware or software is functioning correctlyor at full capacity. In addition, the bandwidth that a particular linkprovides to a stream computing application may vary if the link isshared with other applications and users. The available bandwidth mayvary depending on the load placed on the link by the other applicationsand users. The bandwidth that a particular link provides may also varydepending on a temporal factor, such as time of day, day of week, day ofmonth, or season.

FIG. 2 is a more detailed view of a compute node 110, which may be thesame as one of the compute nodes 110A-110D of FIG. 1, according tovarious embodiments. The compute node 110 may include, withoutlimitation, one or more processors (CPUs) 205, a network interface 215,an interconnect 220, a memory 225, and a storage 230. The compute node110 may also include an I/O device interface 210 used to connect I/Odevices 212, e.g., keyboard, display, and mouse devices, to the computenode 110.

Each CPU 205 retrieves and executes programming instructions stored inthe memory 225 or storage 230. Similarly, the CPU 205 stores andretrieves application data residing in the memory 225. The interconnect220 is used to transmit programming instructions and application databetween each CPU 205, I/O device interface 210, storage 230, networkinterface 215, and memory 225. The interconnect 220 may be one or morebusses. The CPUs 205 may be a single CPU, multiple CPUs, or a single CPUhaving multiple processing cores in various embodiments. In oneembodiment, a processor 205 may be a digital signal processor (DSP). Oneor more processing elements 235 (described below) may be stored in thememory 225. A processing element 235 may include one or more streamoperators 240 (described below). In one embodiment, a processing element235 is assigned to be executed by only one CPU 205, although in otherembodiments the stream operators 240 of a processing element 235 mayinclude one or more threads that are executed on two or more CPUs 205.The memory 225 is generally included to be representative of a randomaccess memory, e.g., Static Random Access Memory (SRAM), Dynamic RandomAccess Memory (DRAM), or Flash. The storage 230 is generally included tobe representative of a non-volatile memory, such as a hard disk drive,solid state device (SSD), or removable memory cards, optical storage,flash memory devices, network attached storage (NAS), or connections tostorage area network (SAN) devices, or other devices that may storenon-volatile data. The network interface 215 is configured to transmitdata via the communications network 120.

A stream computing application may include one or more stream operators240 that may be compiled into a “processing element” container 235. Thememory 225 may include two or more processing elements 235, eachprocessing element having one or more stream operators 240. Each streamoperator 240 may include a portion of code that processes tuples flowinginto a processing element and outputs tuples to other stream operators240 in the same processing element, in other processing elements, or inboth the same and other processing elements in a stream computingapplication. Processing elements 235 may pass tuples to other processingelements that are on the same compute node 110 or on other compute nodesthat are accessible via communications network 120. For example, aprocessing element 235 on compute node 110A may output tuples to aprocessing element 235 on compute node 110B.

The storage 230 may include a buffer 260. Although shown as being instorage, the buffer 260 may be located in the memory 225 of the computenode 110 or in a combination of both memories. Moreover, storage 230 mayinclude storage space that is external to the compute node 110, such asin a cloud.

The compute node 110 may include one or more operating systems 262. Anoperating system 262 may be stored partially in memory 225 and partiallyin storage 230. Alternatively, an operating system may be storedentirely in memory 225 or entirely in storage 230. The operating systemprovides an interface between various hardware resources, including theCPU 205, and processing elements and other components of the streamcomputing application. In addition, an operating system provides commonservices for application programs, such as providing a time function.

FIG. 3 is a more detailed view of the management system 105 of FIG. 1according to some embodiments. The management system 105 may include,without limitation, one or more processors (CPUs) 305, a networkinterface 315, an interconnect 320, a memory 325, and a storage 330. Themanagement system 105 may also include an I/O device interface 310connecting I/O devices 312, e.g., keyboard, display, and mouse devices,to the management system 105.

Each CPU 305 retrieves and executes programming instructions stored inthe memory 325 or storage 330. Similarly, each CPU 305 stores andretrieves application data residing in the memory 325 or storage 330.The interconnect 320 is used to move data, such as programminginstructions and application data, between the CPU 305, I/O deviceinterface 310, storage unit 330, network interface 315, and memory 325.The interconnect 320 may be one or more busses. The CPUs 305 may be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 305 may bea DSP. Memory 325 is generally included to be representative of a randomaccess memory, e.g., SRAM, DRAM, or Flash. The storage 330 is generallyincluded to be representative of a non-volatile memory, such as a harddisk drive, solid state device (SSD), removable memory cards, opticalstorage, Flash memory devices, network attached storage (NAS),connections to storage area-network (SAN) devices, or the cloud. Thenetwork interface 315 is configured to transmit data via thecommunications network 120.

The memory 325 may store a stream manager 134. The stream manager 134can have software features that manage the passthru feature of thestream operator 240. In various embodiments, the stream manager 134 mayhave a stream operator monitor 140, a passthru manager 145, and aselection interface 340.

The stream operator monitor 140 can monitor one or more stream operators240 for an indicative element. The indicative element can be a streamoperator 240 that fails to transmit a tuple with a processed attribute.In one example, a severed connection may cause the stream operator 240to stop transmitting the tuple and become an indicative element. Forexample, the connection between a plurality of stream operators 240,where a first stream operator transmits to a second stream operator, canbe severed or faulty if the second stream operator does not received atuple with a processed attribute from the first stream operator.

The lack of receipt of the tuple can be for a variety of reasons. Forexample, the first stream operator can have an operation that depends onsearching a database. If the connection between the database and thefirst stream operator is severed, then the first stream operator cannotoutput a tuple with a processed attribute to the second stream operator.

The first stream operator can also be indicative of a fault if the firststream operator did not start properly, and did not make a connectionwith the second stream operator. In another example, a connectionbetween the first stream operator and second stream operator could befaulty because of a network problem, a transport or messaging protocolproblem, or some incompatibility between the two operators. This mayoccur even if the first stream operator and second stream operator arefunctional. The lack of a connection can be indicative of a fault on thefirst stream operator, i.e., make the first stream operator anindicative element.

In another example, an indicative element can exist if the streamoperator has a non-performing subroutine that causes the stream operatornot to transmit a tuple with a processed attribute. In variousembodiments, the stream operator monitor 140 can determine if there is aconnection by receiving a signal from the stream operator that indicatesthat the stream operator is transmitting a tuple with a processedattribute.

The indicative element is not limited to stream operators. In variousembodiments, the indicative element can include a processing element andbe referred to as an indicative processing element. For example, aproblem or failure in a stream operator contained in a processingelement can cause the whole processing element to fail. A processingelement can contain a single stream operator or multiple streamoperators which can cause a series of missing connections between streamoperators.

In certain embodiments, the passthru manager 145 can activate thepassthru feature by transmitting a passthru command to an element, e.g.,a processing element or stream operator, adjacent to the indicativeelement, i.e., the adjacent element. The adjacent element can be theelement immediately downstream or upstream from the element. Theadjacent element, e.g., a stream operator or processing element, to theindicative element can receive the passthru command and activate thepassthru feature. The passthru feature can allow the adjacent element todisable or alter processing of an attribute of a tuple and transmit thetuple with either a semi-processed or unprocessed attribute to anotherelement, skipping the indicative element. Throughout the disclosure, theterm element can be used to refer to either a stream operator orprocessing element. The term stream operator can be used interchangeablywith the term element.

The selection interface 340 can be an interface for a user to selectbetween different passthru methods. In various embodiments, the user maygraphically select particular stream operators to enable the passthrufeature. The selection interface 340 may communicate the selection ofthe user to the stream manager 134. In various embodiments, theselection interface 340 can be an optional feature.

Additionally, the storage 330 may store an operator graph 335. Theoperator graph 335 may define how tuples are routed to processingelements 235 (FIG. 2) for processing.

The management system 105 may include one or more operating systems 332.An operating system 332 may be stored partially in memory 325 andpartially in storage 330. Alternatively, an operating system may bestored entirely in memory 325 or entirely in storage 330. The operatingsystem provides an interface between various hardware resources,including the CPU 305, and processing elements and other components ofthe stream computing application. In addition, an operating systemprovides common services for application programs, such as providing atime function.

FIG. 4 is a more detailed view of the compiler system 102 of FIG. 1according to some embodiments. The compiler system 102 may include,without limitation, one or more processors (CPUs) 405, a networkinterface 415, an interconnect 420, a memory 425, and storage 430. Thecompiler system 102 may also include an I/O device interface 410connecting I/O devices 412, e.g., keyboard, display, and mouse devices,to the compiler system 102.

Each CPU 405 retrieves and executes programming instructions stored inthe memory 425 or storage 430. Similarly, each CPU 405 stores andretrieves application data residing in the memory 425 or storage 430.The interconnect 420 is used to move data, such as programminginstructions and application data, between the CPU 405, I/O deviceinterface 410, storage unit 430, network interface 415, and memory 425.The interconnect 420 may be one or more busses. The CPUs 405 may be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 405 may bea DSP. Memory 425 is generally included to be representative of a randomaccess memory, e.g., SRAM, DRAM, or Flash. The storage 430 is generallyincluded to be representative of a non-volatile memory, such as a harddisk drive, solid state device (SSD), removable memory cards, opticalstorage, flash memory devices, network attached storage (NAS),connections to storage area-network (SAN) devices, or to the cloud. Thenetwork interface 415 is configured to transmit data via thecommunications network 120.

The compiler system 102 may include one or more operating systems 432.An operating system 432 may be stored partially in memory 425 andpartially in storage 430. Alternatively, an operating system may bestored entirely in memory 425 or entirely in storage 430. The operatingsystem provides an interface between various hardware resources,including the CPU 405, and processing elements and other components ofthe stream computing application. In addition, an operating systemprovides common services for application programs, such as providing atime function.

The memory 425 may store a compiler 136. The compiler 136 compilesmodules, which include source code or statements, into the object code,which includes machine instructions that execute on a processor. In oneembodiment, the compiler 136 may translate the modules into anintermediate form before translating the intermediate form into objectcode. The compiler 136 may output a set of deployable artifacts that mayinclude a set of processing elements and an application descriptionlanguage file (ADL file), which is a configuration file that describesthe stream computing application. In some embodiments, the compiler 136may be a just-in-time compiler that executes as part of an interpreter.In other embodiments, the compiler 136 may be an optimizing compiler. Invarious embodiments, the compiler 136 may perform peepholeoptimizations, local optimizations, loop optimizations, inter-proceduralor whole-program optimizations, machine code optimizations, or any otheroptimizations that reduce the amount of time required to execute theobject code, to reduce the amount of memory required to execute theobject code, or both. The output of the compiler 136 may be representedby an operator graph, e.g., the operator graph 335.

In various embodiments, the compiler 136 can include the passthrufeature on a particular stream operator on the operator graph 335 duringcompile time by writing the passthru feature onto a particular streamoperator. In various embodiments, the passthru feature may be includedas a default and activated by a passthru command from the stream manager134. The passthru feature may also be included as an optional featurefor a particular stream operator and may be activated by the user.

The compiler 136 may also provide the application administrator with theability to optimize performance through profile-driven fusionoptimization. Fusing operators may improve performance by reducing thenumber of calls to a transport. While fusing stream operators mayprovide faster communication between operators than is available usinginter-process communication techniques, any decision to fuse operatorsrequires balancing the benefits of distributing processing acrossmultiple compute nodes with the benefit of faster inter-operatorcommunications. The compiler 136 may automate the fusion process todetermine how to best fuse the operators to be hosted by one or moreprocessing elements, while respecting user-specified constraints. Thismay be a two-step process, including compiling the application in aprofiling mode and running the application, then re-compiling and usingthe optimizer during this subsequent compilation. The end result may,however, be a compiler-supplied deployable application with an optimizedapplication configuration.

FIG. 5 illustrates an exemplary operator graph 500 for a streamcomputing application beginning from one or more sources 135 through toone or more sinks 504, 506, according to some embodiments. This flowfrom source to sink may also be generally referred to herein as anexecution path. In addition, a flow from one processing element toanother may be referred to as an execution path in various contexts.Although FIG. 5 is abstracted to show connected processing elementsPE1-PE10, the operator graph 500 may include data flows between streamoperators 240 (FIG. 2) within the same or different processing elements.Typically, processing elements, such as processing element 235 (FIG. 2),receive tuples from the stream as well as output tuples into the stream(except for a sink—where the stream terminates, or a source—where thestream begins). While the operator graph 500 includes a relatively smallnumber of components, an operator graph may be much more complex and mayinclude many individual operator graphs that may be statically ordynamically linked together.

The example operator graph shown in FIG. 5 includes ten processingelements (labeled as PE1-PE10) running on the compute nodes 110A-110D. Aprocessing element may include one or more stream operators fusedtogether to form an independently running process with its own processID (PID) and memory space. In cases where two (or more) processingelements are running independently, inter-process communication mayoccur using a “transport,” e.g., a network socket, a TCP/IP socket, orshared memory. Inter-process communication paths used for inter-processcommunications can be a critical resource in a stream computingapplication. However, when stream operators are fused together, thefused stream operators can use more rapid communication techniques forpassing tuples among stream operators in each processing element.

Each processing element may have a passthru manager 145. The passthrumanager 145 may transmit the passthru command to a processing element.The processing element may further transmit or direct the passthrucommand to a targeted stream operator within the processing element.Once the targeted stream operator receives the passthru command, thenthe targeted stream operator may activate the passthru feature. Theoperator graph 132 can encompass one or more processing elements, e.g.,PE2 and PE4 from FIG. 5, which may lie on more than one compute node,e.g., 110A and 110B. In various embodiments, the passthru manager 145can transmit the passthru command to an adjacent processing element ifan indicative processing element is detected. The indicative processingelement can be a processing element that indicates a fault. The adjacentprocessing element can be the processing element downstream or upstreamfrom the indicative processing element and can receive the passthrucommand and passthru the indicative processing element.

The operator graph 500 begins at a source 135 and ends at a sink 504,506. Compute node 110A includes the processing elements PE1, PE2, andPE3. Source 135 flows into the processing element PE1, which in turnoutputs tuples that are received by PE2 and PE3. For example, PE1 maysplit data attributes received in a tuple and pass some data attributesin a new tuple to PE2, while passing other data attributes in anothernew tuple to PE3. As a second example, PE1 may pass some received tuplesto PE2 while passing other tuples to PE3. Tuples that flow to PE2 areprocessed by the stream operators contained in PE2, and the resultingtuples are then output to PE4 on compute node 110B. Likewise, the tuplesoutput by PE4 flow to operator sink PE6 504. Similarly, tuples flowingfrom PE3 to PE5 also reach the operators in sink PE6 504. Thus, inaddition to being a sink for this example operator graph, PE6 could beconfigured to perform a join operation, combining tuples received fromPE4 and PE5. This example operator graph also shows tuples flowing fromPE3 to PE7 on compute node 110C, which itself shows tuples flowing toPE8 and looping back to PE7. Tuples output from PE8 flow to PE9 oncompute node 110D, which in turn outputs tuples to be processed byoperators in a sink processing element, for example PE10 506.

Processing elements 235 (FIG. 2) may be configured to receive or outputtuples in various formats, e.g., the processing elements or streamoperators could exchange data marked up as XML documents. Furthermore,each stream operator 240 within a processing element 235 may beconfigured to carry out any form of data processing functions onreceived tuples, including, for example, writing to database tables orperforming other database operations such as data joins, splits, reads,etc., as well as performing other data analytic functions or operations.

The stream manager 134 of FIG. 1 may be configured to monitor a streamcomputing application running on compute nodes, e.g., compute nodes110A-110D, as well as to change the deployment of an operator graph,e.g., operator graph 132. The stream manager 134 may move processingelements from one compute node 110 to another, for example, to managethe processing loads of the compute nodes 110A-110D in the computinginfrastructure 100. Further, stream manager 134 may control the streamcomputing application by inserting, removing, fusing, un-fusing, orotherwise modifying the processing elements and stream operators (orwhat tuples flow to the processing elements) running on the computenodes 110A-110D.

Because a processing element may be a collection of fused streamoperators, it is equally correct to describe the operator graph as oneor more execution paths between specific stream operators, which mayinclude execution paths to different stream operators within the sameprocessing element. FIG. 5 illustrates execution paths betweenprocessing elements for the sake of clarity.

FIG. 6 illustrates a flowchart of a method 600 of processing a stream oftuples using the passthru feature, according to various embodiments. Themethod 600 can begin at operation 610 where the stream operator monitor140 monitors the operator graph 132 to determine whether a streamoperator is an indicative element. As mentioned previously, anindicative element can be a stream operator that is not transmitting aprocessed attribute.

The stream operator monitor 140 can determine that a stream operator isfaulty if a particular stream operator does not transmit a functionalsignal. The functional signal can be a signal that a stream operatortransmits to the stream operator monitor 140 when it receives a tuplewith a processed attribute from the prior stream operator. For example,if a first stream operator transmits a tuple with a processed attributeto a second stream operator, then the second stream operator cantransmit the functional signal. If there are eight stream operators onthe operator graph 132, then the stream operator monitor 140 shouldreceive eight functional signals. If the stream operator monitor 140receives five functional signals, then some stream operators can befaulty.

The functional signal can depend on other factors besides the receipt ofa processed attribute. For example, the functional signal can depend onwhether a stream operator is able to receive and transmit a tuple with aprocessed attribute. In various embodiments, the operator graph 132 canhalt processing of tuples in response to an absence of a functionalsignal.

An indicative element can be detected without monitoring for afunctional signal. In various embodiments, a stream operator monitor 140can monitor for an indicative element by requiring each stream operatorin an operator graph to keep track of its own status, For example, thestatus of a stream operator can be determined by: a comparison betweenthe number of active connections in the stream operator versus thenumber of assigned connections; whether the stream operator able toperform processing, e.g., if the stream operator has a function where itlooks up a record from a database, is the lookup function working; acomparison of the number of tuples the stream operator sends versus thenumber of tuples the stream operator receives. The stream operatormonitor 140 can receive the status from each stream operator in theoperator graph. If any stream operator has a negative status, then thestream operator monitor 140 can identify the stream operators with anegative status as an indicative element.

In various embodiments, the stream operator monitor 140 can monitordownstream operations. For example, the stream operator monitor 140 cananalyze each attribute of the tuple to determine if the stream operatorprocessed the attribute.

After the stream operator monitor 140 monitors the operator graph 132for the indicative element, then the method 600 can proceed to operation612. In operation 612, the stream operator monitor 140 can determine ifa particular stream operator is faulty. As explained in the exampleabove, the absence of a functional signal can indicate an indicativeelement.

An absence of a functional signal can be due to an internal error in thestream operator. An example of the first stream operator not able totransmit a processed attribute to the second stream operator can occurwhen the first stream operator has a routine that compares the attributewith an external database. If the external database is not functioningor the connection to the external database is non-functional, then thefirst stream operator is not able to transmit a processed attribute andthe first stream operator is indicative of a fault, i.e., an indicativeelement.

In another example, the functional signal can be withheld if the streamoperator is not receiving a stream of tuples due to a connection issue.For example, if a first stream operator is not transmitting a tuple to asecond stream operator, either the first stream operator or the secondstream operator can be considered faulty.

If the stream operator is not faulty, then the method 600 can proceed tooperation 610 where the stream operator monitor 140 monitors theoperator graph for an indicative element. If the stream operator isfaulty, then the method 600 can continue to operation 614. In operation614, the stream manager 134 determines whether the indicative element isessential. Generally, an indicative element is essential when adownstream process on the operator graph 132 depends on the processedattribute from the element. For example, if a first stream operatortranscribes text from an image attribute, and a second stream operatortakes an attribute containing the transcribed text and compares the textagainst a database, then the first stream operator can be considered anessential indicative element. In the above example, the second streamoperator may not be an essential stream operator if there are no otherstream operators in the operator graph 132 that depend on it.

A stream operator may be considered essential based on user parameters.Using the mentioned example, if the user indicates that the compareagainst the database step may be skipped, then the first stream operatorcan be considered non-essential. Assuming that the stream manager 134encounters an essential indicative element, then the stream manager 134can deactivate the operator graph 132 so that the operator graph 132stops the processing of tuples in operation 618. Operation 618 can causethe operator graph 132 to stop receiving the tuples which can send anindication to the user or the system 100 that the operator graph 132 isno longer functional. In various embodiments, the user can override thefailure and cause the operator graph 132 to continue processing tuples.

If the stream operator is found to be non-essential, then the operation614 can proceed to operation 616. In operation 616, the stream manager134 can determine whether the passthru feature on the operator graph 132is enabled. In some circumstances, the user can make a determination todisable the passthru feature. For example, if the user requires everyattribute to be processed, then the user can disable the passthrufeature. The above example can be particularly important in applicationswhere it is important to process all attributes such as in criminalrecords processing, or in tax analysis. In various embodiments, thepassthru feature can be activated by default. If the passthru feature isnot enabled, then the method 600 may proceed to operation 618 where theoperator graph 132 fails.

If the passthru feature is enabled, then the method 600 can proceed tooperation 620. In operation 620, the passthru method is presented to theuser via a selection interface 340 to select from a list of possiblepassthru methods and is further discussed below. A sample of possiblepassthru methods is provided on FIG. 7. The passthru methods includewhere the stream operator writes a reference attribute 718, where thestream operator passes tuple through without processing 720, and where adummy stream operator is formed to process tuples 722. Each of thesepassthru methods are described further below.

The stream manager 134 determines the passthru method to implement inoperation 620. After operation 620, the method 600 can proceed tooperation 622. In operation 622, the selected passthru method isimplemented. In various embodiments, the passthru manager 145 can beresponsible for generating a passthru command that corresponds to theselected passthru method obtained from operation 620. The passthrucommand can be a sequence of instructions that is generated by thepassthru manager 145. The passthru command can be further received bythe adjacent element to the indicative element to initiate a passthrumethod.

In some embodiments, the passthru command can be generated by thepassthru manager 145 and received by the stream manager 134 as anintermediary. For example, in particular passthru methods, e.g., createa dummy stream operator, other components of the system 100 can berequired to implement the passthru command. The stream manager 134 canreceive the passthru command corresponding to creating a dummy streamoperator and the stream manager 134 can accesses the compiler 136 wherethe compiler 136 creates the dummy stream operator. In variousembodiments, the stream manager 134 may create the dummy stream operatorin runtime.

FIG. 7 illustrates a flowchart of a method 700 of selecting a passthrumethod, according to various embodiments. The method 700 can correspondto operation 620 of FIG. 6. The method 700 can begin at operation 710,where the stream manager 134 determines a presence of a default passthrumethod and whether to select the default passthru method. In variousembodiments, a default passthru method may be selected by the user as adefault setting for the passthru feature. For example, the user candictate that, when the passthru feature is activated, a dummy streamoperator should be formed. The default selection may occur without anyfurther user input. If there is a default passthru method, then theoperation proceeds to operation 712. In operation 712, the streammanager 134 can select the passthru method and proceed to operation 622.

If there is not a default passthru method, then the method 700 canproceed to operation 714. In operation 714, the stream manager 134 maydetermine the resources of the computing system 100 for the givenoperator graph 132. The resources can be in the form of a performancefactor, which can represent a numerical score of performance. Forexample, the performance factor may weigh different aspects of theoperator graph 132 such as processing time, bandwidth, network bandwidthbetween compute nodes, or memory usage. Each of the factors can have theweights customized by the users. In various embodiments, the resourcesof the computing system 100 underlying the operator graph 132 canmeasure the performance factor without regard to a numerical score.

The resources can be further used to determine a threshold. For example,if the slowest passthru method has a processing time of 10 ms, then thethreshold may be a processing time of 10 ms. The threshold can also beuser customizable to reflect certain goals. For example, the user canspecify that the processing time be under 2 ms to include only thefastest passthru method.

After the resources of the computing system 100 are determined, then themethod 700 can proceed to operation 716. In operation 716, the passthrumethod is selected. In various embodiments, the passthru method can beselected by the stream manager 134 based on the predicted performance ofa passthru method. The performance can be predicted using criteria suchas past performance or a performance history. In various embodiments,the passthru method is selected by the user via a selection interface340. The section interface 340 may be a graphical user interface wherethe user can select from one or more passthru methods. For example, theuser may be prompted with the choice of one or more passthru methods.The passthru methods can include where the stream operator writes areference attribute 718, where the stream operator passes tuples throughwithout processing 720, and where a dummy stream operator is formed toprocess tuples 722. Each of the described passthru methods may bedescribed in FIG. 8.

FIG. 8 illustrates an operator graph 800 that uses one or more passthrufeatures, according to various embodiments. The operator graph 800 canbe an example of the operator graph 132 from FIG. 1. The operator graph800 receives a stream of tuples from a source 135. Stream operator 805can receive a tuple 803 with four attributes in the three illustratedpassthru methods. Stream operator 805 can process one or more of theattributes. For example, if the operator graph represents the activityof a tollbooth and attribute 4 (described as ATT4-I) represents an imagecapture of a license plate, then stream operator 805 can undertake thetranscription of a license plate image. The stream operator 805 cantransmit the tuple 804 with the transcribed image of the license plate(described as ATT4-T).

Continuing with the tollbooth example, the stream operator 810 can havethe function of receiving the tuple 804 with the transcribed licenseplate ATT4-T from stream operator 805 and comparing it to a database ofstolen cars. If the communication with the database is not working, thenstream operator 810 can be considered an indicative element.

The operation of passthru method 718 can occur when the stream operator810 writes a reference attribute. The stream operator 810 can generate areference attribute that indicates that the database search was notconducted. In the illustration, the letter X is used to show a generatedattribute that provides such an indication. In various embodiments, thestream operator 810 can use the reference attribute X to refer back tothe omitted attribute. For example, in the tollbooth example, the streamoperator 810 can transmit the reference attribute X through the operatorgraph 800. The reference attribute X, when used by a stream operator,e.g., stream operator 820, can point back to the location of thetranscribed license plates (such as a table of transcribed licenseplates).

In various embodiments, the reference attribute X can be a transcribedlicense plate along with an indication that the transcribed licenseplate was not compared to the database. A stream operator, e.g., streamoperator 820, which reads that the indication can know that thetranscribed license plate was not compared to the database. After thereference attribute X is substituted in stream operator 810, then thetuple 807 can continue to stream operator 820. Stream operator 820 canprocess one or more attributes from the tuple 807. In variousembodiments, the stream operator 820 can process the reference attributeX. After the tuple 807 goes to stream operator 820, the tuple 807 can betransmitted to a sink 825.

The operation of passthru method 720 can occur when the indicativeelement 810 transmits a tuple 804 without processing the attribute. Thestream operator 810 can receive the tuple 804, and upon finding out thatthe license plate cannot be compared to the database, transmit the tuple804 with attribute ATT4-T to stream operator 820. In variousembodiments, the attribute ATT4-T does not have an indication that ithas been processed. For example, if a true or false results from streamoperator 810 to indicate that the car is stolen or not stolen, then adownstream operator, e.g., stream operator 820, can receive thetranscribed license plate ATT4-T instead of the true or false result.Stream operator 820 can process one or more attributes from the tuple804 and transmit the tuple 804 to the sink 825.

The operation of passthru method 722 can occur when the stream manager134 or the compiler 136 creates a dummy stream operator 815. Once theindicative element 810 is detected, then the stream manager 134 maycreate a dummy stream operator 815 to process the tuple. The dummystream operator 815 can reroute the stream of tuples which can reducethe processing delay from the passthru. The dummy stream operator 815can receive the tuple 804 and be configured to specifically transmit thetuple 804 to the stream operator 820 in a similar manner to passthrumethod 720.

The methods 718, 720, and 722 can be initiated using the passthrucommand. The passthru command can be received by an adjacent element. Inthe operator graph 800, the adjacent elements are stream operator 805and stream operator 820. In various embodiments, the passthru commandcan be received by stream operator 805 which can initiate a passthrumethod. For example, if the stream operator 805 receives the passthrucommand corresponding to method 718, then the stream operator 805 canpassthru the tuple 807 with attribute X to stream operator 820. Thepassthru command can also be received by stream operator 820 that canrequest from stream operator 805 or the stream manager 134 to receivethe tuple 807 directly from stream operator 805. In various embodiments,an operator graph can require that both adjacent elements receive thepassthru command.

Even though the operator graph 800 depicts the stream operator 810 astransmitting to one stream operator, e.g., stream operator 820, thestream operator 810 can transmit and be received by more than one streamoperator. In this example, the stream operator 805 can passthru a tupleto the receiving stream operators.

Returning to FIG. 7, once the user or the stream manager 134 selects apassthru method in operation 716, then the method 700 can proceed tooperation 724. Operation 724 can be considered an optional step. Invarious embodiments, the result in operation 716 may override the resultof operation 724. For example, the selected passthru method from a usermay have a higher weight than the selected passthru method from thestream manager 134.

In operation 724, the stream manager 134 can measure the resourcesrequired to use particular passthru method, i.e., the performance of thepassthru method. In various embodiments, a simulation may be used topredict the performance of using a passthru method. This may beaccomplished, for example, by using a simulation engine. A simulationengine can simulate the effect of various scenarios regarding passthrumethod usage. In various embodiments, the simulation engine may simulatethe performance of multiple passthru methods as one step and compareeach passthru method to the threshold.

The performance history can be used to predict the performance of thepassthru method. For example, the stream manager 134 can measure theresources required by the operator graph 132 if passthru method 718 hada processing time of 5 ms but passthru method 722 had a processing timeof 2 ms in previous runs.

The values for the predicted performance or resource usage for aparticular passthru method can be compared to the threshold for theoperator graph 132. If the predicted performance is greater than thethreshold, then the method 700 may continue to operation 716 where a newpassthru method can be selected. In various embodiments, if the passthrumethod was selected by the stream manager 134, the passthru method maybe selected by the user.

If the predicted performance of the passthru method is less than thethreshold, then the method 700 may continue to operation 726. Operation726 may be optional, where the selected passthru method is associatedwith a default method. For example, if the stream manager 134 determinesthat the passthru method 722 meets the threshold and is selected by theuser, then the stream manager 134 may associate passthru method 722 withthe default passthru method whenever the passthru feature is enabled.After operation 726, the method 700 can proceed to operation 712 wherethe passthru method is selected for use by the stream manager 134.

In the foregoing, reference is made to various embodiments. It should beunderstood, however, that this disclosure is not limited to thespecifically described embodiments. Instead, any combination of thedescribed features and elements, whether related to differentembodiments or not, is contemplated to implement and practice thisdisclosure. Furthermore, although embodiments of this disclosure mayachieve advantages over other possible solutions or over the prior art,whether or not a particular advantage is achieved by a given embodimentis not limiting of this disclosure. Thus, the described aspects,features, embodiments, and advantages are merely illustrative and arenot considered elements or limitations of the appended claims exceptwhere explicitly recited in a claim(s).

Aspects of the present disclosure may be embodied as a system, method,or computer program product. Accordingly, aspects of the presentdisclosure may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.), or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “circuit,”“module,” or “system.” Furthermore, aspects of the present disclosuremay take the form of a computer program product embodied in one or morecomputer readable medium(s) having computer readable program codeembodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination thereof. More specificexamples (a non-exhaustive list) of the computer readable storage mediumwould include the following: an electrical connection having one or morewires, a portable computer diskette, a hard disk, a random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), an optical fiber, a portable compactdisc read-only memory (CD-ROM), an optical storage device, a magneticstorage device, or any suitable combination thereof. In the context ofthis disclosure, a computer readable storage medium may be any tangiblemedium that can contain, or store, a program for use by or in connectionwith an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wire line, optical fiber cable, RF, etc., or any suitable combinationthereof.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including: (a) an object oriented programminglanguage; (b) conventional procedural programming languages; and (c) astreams programming language, such as IBM Streams Processing Language(SPL). The program code may execute as specifically described herein. Inaddition, the program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer, or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork, including a local area network (LAN) or a wide area network(WAN), or the connection may be made to an external computer (forexample, through the Internet using an Internet Service Provider).

Aspects of the present disclosure have been described with reference toflowchart illustrations, block diagrams, or both, of methods,apparatuses (systems), and computer program products according toembodiments of this disclosure. It will be understood that each block ofthe flowchart illustrations or block diagrams, and combinations ofblocks in the flowchart illustrations or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing the functionsor acts specified in the flowchart or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function or act specified in the flowchart or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus, or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions or acts specified in the flowchart or blockdiagram block or blocks.

Embodiments according to this disclosure may be provided to end-usersthrough a cloud-computing infrastructure. Cloud computing generallyrefers to the provision of scalable computing resources as a serviceover a network. More formally, cloud computing may be defined as acomputing capability that provides an abstraction between the computingresource and its underlying technical architecture (e.g., servers,storage, networks), enabling convenient, on-demand network access to ashared pool of configurable computing resources that can be rapidlyprovisioned and released with minimal management effort or serviceprovider interaction. Thus, cloud computing allows a user to accessvirtual computing resources (e.g., storage, data, applications, and evencomplete virtualized computing systems) in “the cloud,” without regardfor the underlying physical systems (or locations of those systems) usedto provide the computing resources.

Typically, cloud-computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g., an amount of storage space used by a useror a number of virtualized systems instantiated by the user). A user canaccess any of the resources that reside in the cloud at any time, andfrom anywhere across the Internet. In context of the present disclosure,a user may access applications or related data available in the cloud.For example, the nodes used to create a stream computing application maybe virtual machines hosted by a cloud service provider. Doing so allowsa user to access this information from any computing system attached toa network connected to the cloud (e.g., the Internet).

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams or flowchart illustration, andcombinations of blocks in the block diagrams or flowchart illustration,can be implemented by special purpose hardware-based systems thatperform the specified functions or acts, or combinations of specialpurpose hardware and computer instructions.

Although embodiments are described within the context of a streamcomputing application, this is not the only context relevant to thepresent disclosure. Instead, such a description is without limitationand is for illustrative purposes only. Additional embodiments may beconfigured to operate with any computer system or application capable ofperforming the functions described herein. For example, embodiments maybe configured to operate in a clustered environment with a standarddatabase processing application. A multi-nodal environment may operatein a manner that effectively processes a stream of tuples. For example,some embodiments may include a large database system, and a query of thedatabase system may return results in a manner similar to a stream ofdata.

While the foregoing is directed to exemplary embodiments, other andfurther embodiments of the disclosure may be devised without departingfrom the basic scope thereof, and the scope thereof is determined by theclaims that follow.

What is claimed is:
 1. A computer program product for processing astream of tuples, each tuple containing one or more attributes, thecomputer program product comprising a computer readable storage mediumhaving program code embodied therewith, the program code comprisingcomputer readable program code configured to: process the attributes ofthe stream of tuples using stream operators operating on one or morecomputer processors and corresponding one or more processing elements;detect an indicative element from a plurality of stream operators;transmit, in response to detecting the indicative element, a passthrucommand to a processing element corresponding to the indicative element,wherein the transmit the passthru command includes: determine aperformance factor of the passthru command on the processing element,determine a threshold based on resources of a computing system, andselect, in response to the performance factor falling within thethreshold, the passthru command; and alter, in response to receiving thepassthru command at the processing element, a portion of attributeprocessing for the indicative element.
 2. The computer program productof claim 1, wherein the detect an indicative element includes: determinean essential indicative element; and disable, in response to finding theessential indicative element, the plurality of stream operators.
 3. Thecomputer program product of claim 1, wherein the transmit a passthrucommand includes: determine whether a passthru feature is enabled; anddisable, in response to the passthru feature not being enabled, theplurality of stream operators.
 4. The computer program product of claim1, wherein select the passthru command includes: select, in response toa presence of a default passthru method, the passthru command thatcorresponds to the default passthru method.
 5. The computer programproduct of claim 1, wherein select the passthru command furtherincludes: associate a selected passthru command with a default passthrumethod.
 6. The computer program product of claim 1, wherein the passthrucommand is selected from writing a reference attribute, passing thetuple through the stream operator, and forming a dummy stream operator.7. A system for processing a stream of tuples, each tuple having one ormore attributes, comprising: one or more processing elements operatingon one or more computer processors configured to processes theattributes from the stream of tuples; a stream manager configured toalter a portion of attribute processing for an indicative element inresponse to the processing element receiving a passthru command, furthercomprising: a stream operator monitor configured to detect theindicative element from a plurality of stream operators in the one ormore processing elements; a passthru manager configured to transmit thepassthru command in response to detecting the indicative element,wherein the stream manager is configured to transmit a passthru commandby: determining a performance factor of the passthru command on theprocessing element; determining a threshold based on resources of acomputing system; and selecting, in response to the performance factorfalling within the threshold, the passthru command.
 8. The system ofclaim 7, the stream manager further comprising: a selection interfaceconfigured to present to a user, in response to detecting the indicativeelement, a plurality of passthru methods that are associated withpassthru commands for selection.